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20172025
most citedA quasi-Monte Carlo Method for an Optimal Control Problem Under Uncertainty

7 citations · 13 across the 2 of their papers we have counts for

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11 papers · 1 filter

math.NA2025

Sufficient conditions for QMC analysis of finite elements for parametric differential equations

Vesa Kaarnioja, Andreas Rupp, Jay Gopalakrishnan

Parametric regularity of discretizations of flux vector fields satisfying a balance law is studied under some assumptions on a random parameter that links the flux with an unknown…

math.NA2025

On the optimality of dimension truncation error rates for a class of parametric partial differential equations

Philipp A. Guth, Vesa Kaarnioja

In uncertainty quantification for parametric partial differential equations (PDEs), it is common to model uncertain random field inputs using countably infinite sequences of indepe…

math.NA2025

Quasi-Monte Carlo for Bayesian shape inversion governed by the Poisson problem subject to Gevrey regular domain deformations

Ana Djurdjevac, Vesa Kaarnioja, Max Orteu +1

We consider the application of a quasi-Monte Carlo cubature rule to Bayesian shape inversion subject to the Poisson equation under Gevrey regular parameterizations of domain uncert…

math.NA2025

Uncertainty quantification for stationary and time-dependent PDEs subject to Gevrey regular random domain deformations

Ana Djurdjevac, Vesa Kaarnioja, Claudia Schillings +1

We study uncertainty quantification for partial differential equations subject to domain uncertainty. We parameterize the random domain using the model recently considered by Chern…

math.NA2025

Lattice Rules Meet Kernel Cubature

Vesa Kaarnioja, Ilja Klebanov, Claudia Schillings +1

Rank-1 lattice rules are a class of equally weighted quasi-Monte Carlo methods that achieve essentially linear convergence rates for functions in a reproducing kernel Hilbert space…

math.NA2024

Uncertainty quantification for electrical impedance tomography using quasi-Monte Carlo methods

Laura Bazahica, Vesa Kaarnioja, Lassi Roininen

The theoretical development of quasi-Monte Carlo (QMC) methods for uncertainty quantification of partial differential equations (PDEs) is typically centered around simplified model…